Predicting meningococcal meningitis outbreaks in Niger: current progress

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1 Predicting meningococcal meningitis outbreaks in Niger: current progress Laurence Cibrelus, for the study group 2 nd MERIT Meeting, Ethiopia, December 2008 Study group: Jean-François Jusot, Hugo Oliveros, Saacou Djibo, Jean-Pierre Olivier de Sardan, Madeleine Thomson, William Perea, Eric Bertherat

2 Objective To identify and describe the epidemic drivers of meningitis outbreaks over time In line with CERMES* long-term commitment to meningitis surveillance and research in Niger *Centre de Recherche Medicale et Sanitaire, Niger Pasteur Institute

3 A multiple phase perspective Phase 1: Qualitative study by key informants Characterize local patterns of meningitis outbreaks Identify relevant phenomena associated with meningitis outbreaks in Niger Phase 2: Quantitative study Meet with local experts to create indicators based on phenomena identified in Phase 1 Model the occurrence of meningitis outbreaks based on these variables Phase 3: Real time field evaluation of the prediction model in Niger Phase 4: Extension to other countries of the African Meningitis Belt

4 Phase 1: methods Literature review Emphasis on ecological rather than individuals risk factors for meningitis Tested and suggested drivers Qualitative study by key informants 25 meningitis experts in Niger Identified through the literature or by their peers Biology, clinical practice, epidemiology, health policy Face-to-face interviews Standard questionnaires, open-ended questions

5 Outbreak of meningococcal meningitis - Exposition to the germ (droplets) - Pharyngeal colonisation/invasion - Blood diffusion to the brains (disease) Non vaccinated adult = susceptible Neisseria meningitidis (germ) = at risk Human-to-human transmission Immunized adults = protected OUTBREAK Immunized children = protected

6 Thematic data analysis Experts quotes e.g. Acute respiratory infections are more frequent when the Harmattan blows. These infections are responsible for a pharyngeal irritation that increases the risk of meningitis. Identified variables e.g. Most of the meningitis outbreaks occur in the Southern region of the country, by the Nigerian border. This is a virtual border anyway. The same populations live on both sides. People would cross the border to attend social events or during the transhumance. Combined variables in theme categories e.g. Food shortage may contribute to meningitis outbreaks. Besides, meningitis outbreaks are frequent in the region of Maradi which is severely hit by drought.

7 Thematic data analysis Experts quotes Identified variables e.g. Acute respiratory infections, transhumance, drought, Harmattan wind etc Combined variables in theme categories

8 Thematic data analysis Experts quotes Identified variables Combined variables in theme categories e.g. Human settings: transhumance Health : susceptibility to the disease Climate: Harmattan

9 Potential human drivers for meningitis # Phenomenon identified by the qualitative study Variable (type) Possible ascertainments 1 Promiscuity Type of housing, impacting promiscuity High population density Urban or rural settings, impacting education, access to health care 2 Children 3 4 Education, impacting the willingness to seek medical care Health education Poverty, impacting promiscuity and education Financial, pastoral and agricultural resources 5 Area with predominant pastoral or agricultural production Being a Touareg, wearing a turban Ethnic group Mass meetings and gatherings: Weddings, baptisms, religious celebrations, pilgrimages, festivals Season of weddings and baptisms Mass migrations: Exodus Seasonal transhumance within Niger or to neighboring countries 9 Border with Nigeria or Burkina Faso, mostly; or border with Mali Population density (continuous) Proportion of population under 15y (continuous) Level of education (categorical) Vulnerability index (categorical) Type of agricultural production* (categorical) Cultural or religious mass gatherings Transhumance season (categorical) Border with a foreign country (categorical) Census and projections Census and projections Census and projections Routine data Map Calendar Calendar Map *the vulnerability index is weighted according to this variable

10 Potential health drivers for meningitis # Phenomenon identified by the qualitative study Pharyngeal irritation Upper respiratory tract infection Viral lower respiratory tract infection Influenza Immunodeficiency: HIV/AIDS, diabetes, complement deficiency Malnutrition Drought, food shortage Recent epidemic history Immunity/ susceptibility to meningitis Efficiency of the vaccine coverage Variable (type) Incidence of acute respiratory infections* (continuous) Vulnerability index *** (categorical) Incidence of moderate and/or severe malnutrition (continuous) # of year since last meningitis outbreak (continuous) # of year since last immunization campaign (continuous) Vaccine coverage at the last immunization campaign (continuous) Possible ascertainments Routine population-based surveillance data Routine data Routine population-based surveillance data Survey data 5 Chronic carriage: Rate, serogroup Circulating serogroup of Nm Virulence of the serogroup Germ resistance in the environment Rapid case diagnosis and notification Access to health infrastructures Trained health care workers Availability of rapid diagnostic tests Preparedness for outbreak response Early outbreak response Circulating serogroup of Nm (categorical) Proportion of primary health centers using rapid diagnostic tests (categorical) Routine population-based surveillance data Routine data 8 Epidemic spread from a neighboring area Meningitis outbreak in neighboring district, in Niger or in a neighboring country (categorical) Routine population-based surveillance data *at the time the study is conducted, there is no surveillance for influenza; a sentinel surveillance system is expected to be implemented in 2009

11 Potential environmental drivers for meningitis # 1 Phenomenon identified by the qualitative study Heat Thermal amplitude (Day-to-night temperature swing) 2 Rainfall 3 Humidity 4 Harmattan wind 5 Dusty environment and wind 6 Vegetation Variable (type) Temperature (continuous) Total rainfall (continuous) Absolute or relative humidity (continuous) Harmattan wind (categorical) Dust (continuous) Land cover type (categorical) Possible ascertainments Meteorological station ARGOS devices Ground data - Meteorological stations - ARGOS devices Satellite derived estimates Meteorological station ARGOS devices Inter-tropical discontinuity (ITD) Ground data: Meteorological stations Visibility Granularity Satellite derived estimates : daily aerosol index Satellite derived estimate NDVI

12 Phase 2: Quantitative study

13 Outcome of interest Occurrence of meningitis outbreaks (binary) National registry of suspected cases: Weekly incidence of meningitis at the district level Alert and epidemic thresholds based on this incidence Alert: 5 to 9 cases/100,000 pop/week Epidemic: 10 or more cases/100,000 pop/week Decision-making is based on these thresholds

14 Explanatory variables

15 Different scales and coverage - Unclear ascertainment for some variables

16 Type of study Retrospective observational ecological study Unit of analysis: district (administrative level two) Time-trend analysis: forecasting Sources of data: routinely collected information Pilot study: most informative places Source population: entire population of selected districts Secondary data analysis & operational/pragmatic perspective Tailor the methodology to the available information

17 Statistical Analysis Exploratory analysis Descriptive statistics & plots Time-series analysis ARIMA approach Model evaluation FINAL MODEL

18 Exploratory analysis Select relevant time period and geographic areas Eg: vaccine coverage available at the district level from only and in a limited number of districts, while it is a critical predictor Maradi: all variables available except visibility Tailor the methodology to the ultimate aim: an operational decision-making tool to be used by the MoH using existing and readily available data

19 Statistical Analysis Exploratory analysis Descriptive statistics & plots Time-series analysis ARIMA approach Model evaluation FINAL MODEL

20 Weekly incidence of meningitis Weekl y i nci dence of meni ngi t i s Epidemic threshold Week

21 Transhumance season

22 Vaccine coverage*, *# doses of vaccine over 3 years /district population

23 Inter Tropical Discontinuity

24 Statistical Analysis Exploratory analysis Descriptive statistics & plots Challenge Time-series analysis ARIMA approach Model evaluation FINAL MODEL

25 Statistical Analysis Exploratory analysis Descriptive statistics & plots Time-series analysis Logistic regression with time-lagged variables Simple logistic regression Multiple logistic regression Scale check Interaction Goodness of fit FINAL MODEL

26 Time lagged dataset Time W1 W2 W3 Outco me Expos ure Time W1 W2 W3 Outco me Expos ure Time W1 W2 Outco me Expos ure W3

27 Preliminary main effect model: Simple logistic regression with variables lagged over the last 12 weeks Outcome modeled is meningitis outbreak =1 All variables that meet the Wald X 2 test threshold of p-value =0.25 will be included in a multiple logistic regression model Whether time lagged or not Rationale for p=0.25 at this stage: Empirical evidence by Mickey and Greenland (1989) P-value is too small: may fail to identify variables known to be relevant P-value is too small: may include variables of questionable relevance

28 Variables significant at the 25% level

29 Variables significant at the 25% level

30 Variables significant at the 25% level

31 Variables significant at the 25% level

32 Variables significant at the 25% level

33 Methodological issues to be addressed Is time-series analysis relevant with such dataset and such objective? How about the ARIMA approach? Basic ARIMA model? Seasonal ARIMA model? Is multiple logistic regression using time lagged variables relevant? Which time lag is relevant? Two-weeks time-lag (time for the vaccine to be efficient) Three weeks time-lag (implementation of the immunization campaign +vaccine efficiency) Shall we combine time-series analysis to a multiple-group approach, in order to address changes over time within groups and differences between groups (e.g. circulating serogroup)? Is SAS adequate? If so, what is the syntax? Any other comments/recommendations/hints?

34 ACMAD: Tinni Seydou AGRHYMET: Herve Trebossen Acknowledgements CERMES:, Jean-Marc Collard, Jocelyne Rocourt, Noémie Phulpin, the entire Epi department and staff members DMN: Kateillou Lawan DSSRE/SNIS: Zaneidou Mamane INS: Sani Oumarou IRI: Madeleine Thomson, Hugo Oliveros, Sylwia Trzaska, Susana Adamo LASDEL: Jean-Pierre Olivier de Sardan Mailman School of Public Health: Patrick Kinney, Shuang Wang, Mary-Beth Terry Ministry of Agriculture: Mahamadou Saley OMS: Soga Garba; William Perea, Eric Bertherat SAP: Yakoubou Mounkara Meningitis experts

35 Thank you for your attention

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